Review on building energy model calibration by Bayesian inference

Hou, Danlin; Hassan, Ibrahim; Wang, L.

doi:10.1016/j.rser.2021.110930

Cited by 57 publications

(18 citation statements)

References 78 publications

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“…Urban energy modelling is no exception, and this step is required to improve the precision of the results and bring the virtual energy model closer to reality. For urban energy modelling calibration, Bayesian inference is "the process of fitting a probability model to a set of data and summarizing the results by a probability distribution on parameters of the model and unobserved quantities such as predictions of new observations" [50]. It is gaining interest and has been studied by different researchers in recent years.…”

Section: Urban Building Energy Modellingmentioning

confidence: 99%

Integration of PV Systems in the Urban Environment—A Review

Rodrigues,

Palmero-Marrero,

Oliveira

2024

Preprint

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Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The urban heat island development is introduced and related with the existence of PV systems. It is concluded that data acquisition is still a weakness in urban building energy modelling, especially in what concerns non-geometric data, which leads to a major source of errors. The availability of data is a challenge because frequently they are not accessible, due to privacy, high-cost or other reasons. Also, methodologies to develop precise archetypes for creating urban building energy models are lacking.

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Section: Urban Building Energy Modellingmentioning

confidence: 99%

Integration of PV Systems in the Urban Environment—A Review

Rodrigues,

Palmero-Marrero,

Oliveira

2024

Preprint

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“…This uncertainty is often referred to as an "energy performance gap" in literature (Cozza et al, 2021). Effective management of the validation process is crucial for establishing a credible BES model (Hou et al, 2021). However, Malhotra et al reported that among 72 reviewed articles, only 7% conducted a validation study, indicating a limited practice (Malhotra, 2022).…”

Section: Sasbementioning

confidence: 99%

A systematic approach to manual calibration and validation of building energy simulation

Tomrukcu,

Kizildag,

Avgan

et al. 2024

SASBE

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PurposeThis study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.Design/methodology/approachA standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO2 levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.FindingsDiscrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO2 levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.Originality/valueThis paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

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“…Under the ideal situation, all the uncertain parameters in the BEM calculation model should be calibrated [20]. However, due to the data quantity/quality limitation, it is a common practice to identify the uncertain parameters using the SA technique [30].…”

Section: Step 1: Sensitive Analysismentioning

confidence: 99%

“…It has been applied for different building functions (i.e., residential buildings [16], commercial buildings [17]) and different scales of buildings (individual buildings, building clusters [18] and building stocks [19]). Additional focus was put on the statistic performance of different emulators or surrogates under a BC framework [20]. Lim and Zhai [21] compared five meta-models for BC and indicated that the Gaussian-process (GP) model is relatively accurate.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

et al. 2022

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Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.

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Review on building energy model calibration by Bayesian inference

Cited by 57 publications

References 78 publications

Integration of PV Systems in the Urban Environment—A Review

Integration of PV Systems in the Urban Environment—A Review

A systematic approach to manual calibration and validation of building energy simulation

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

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